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The Second Workshop on Digital Disease Detection using Social Media 2025 (2025 DDDSM)
The Second Workshop on Digital Disease Detection using Social Media 2025 (2025 DDDSM)
Announcement (25 August 2025) :
We are delighted to share that authors of outstanding and innovative papers presented at this workshop will have the opportunity to be considered for inclusion in a special collection of BMJ Health & Care Informatics.
Selected submissions will undergo a thorough peer-review process in accordance with the journal’s standards.
For submission guidelines and eligibility criteria, please visit this link.
Announcement (5 August 2025):
We are pleased to announce the opportunity for authors of high-quality and innovative papers presented at this workshop to be considered for publication in a special collection of npj Digital Public Health.
Selected works will undergo a rigorous peer-review process aligned with the journal's standards.
For details on submission guidelines and eligibility criteria, please refer to the following link:
The COVID-19 pandemic has accelerated the shift from traditional epidemiological surveillance to digital disease detection systems, largely driven by the widespread adoption and utilization of social media platforms. Conventional surveillance methods rely heavily on clinical data and laboratory confirmation, often resulting in delayed alerts and responses to emerging outbreaks. In contrast, digital disease detection leverages advanced computational methodologies, including natural language processing (NLP) and large language models, to rapidly analyze extensive social media datasets, offering early indicators of health crises, often weeks ahead of traditional methods.
Recent global health events, notably the Mpox and COVID-19 outbreaks, have underscored the critical role that social media platforms can play in disease surveillance. Platforms such as Twitter have demonstrated the ability to track real-time public health trends, monitor public sentiments, and disseminate critical health information effectively. Furthermore, the incorporation of artificial intelligence (AI) and machine learning techniques into disease detection has proven effective in identifying diverse health conditions, including mental health disorders. Building on the success of the initial DDDSM workshop at the International Joint Conference on Natural Language Processing (IJCNLP) held in Taipei, Taiwan in 2017, we invite submissions for the DDDSM 2025 workshop. This workshop aims to continue exploring how advanced computational methods applied to social media data can enhance early outbreak detection, inform public health responses, and address the ethical considerations involved in digital surveillance.
Objectives of 2025 DDDSM:
Objectives of 2025 DDDSM:
Disseminate recent advances and best practices in social media-based disease surveillance.
Raise awareness among public health communities regarding the benefits and challenges of digital platforms.
Foster interdisciplinary collaborations among researchers, public health practitioners, and policymakers.
Exchange of insights, experiences, and innovative methods for utilizing social media data for health surveillance.
Address ethical, privacy, and responsible usage challenges associated with digital health monitoring.
Workshop Format:
Workshop Format:
The 2025 DDDSM workshop features a single 90-minute session comprising five rigorously reviewed high-quality research papers. Each paper presentation will last approximately 15 minutes, followed by discussions to facilitate interactive engagement with attendees.
Submission Information:
Submission Information:
Full papers (10-12 pages, 4000-5000 words)
Please refer to https://www.sredhconsortium.org/sredh-workshops/2025-dddsm/submission-information for additional details.
We look forward to your participation and valuable contributions to the 2025 DDDSM, advancing the forefront of digital disease detection to improve global health outcomes.
Publication of Accepted Papers :
Publication of Accepted Papers :
The accepted papers will be published as special issue in peer-reviewed medical informatics, health informatics or digital health journal.
** Final date and venue**
Time: Based on Taiwan Time Zone (GMT+8) Friday, August 9,
Venue: Taipei International Convention Center (TICC), Taipei, Taiwan 2025 103, 1F
** Agenda **
Super Theme:
TRACK 3: Health Data Science & Artificial Intelligence
Theme: Theme 2 - Applications
Organisers:
Yung-Chun CHANG, Jitendra JONNAGADDALA, Hong-Jie DAI, Shuntaro YADA
Time: 11:00-1:30 (GMT+8)
Chair(s): Yung-Chun CHANG
11:00 - 11:15
Welcome and overview
11:15 - 11:40
Jitendra JONNAGADDALA (Australia)
Presentation topic: Social listening for digital public health surveillance
11:40 - 12:05
Ching-Tai CHEN (Taiwan)
Presentation topic: A prompt engineering approach to identify protein-protein interactions from biomedical literature
12:05 - 12.25
Yung-Chun Chang
Presentation topic: AI-Powered Clinical NLP: Enhancing Lung Cancer Treatment and Emergency Care Workflows
12:25 - 12:45
Shuntaro YADA (Japan)
Presentation topic: NTCIR-18 RadNLP on Document Segmentation and Document Classification for Lung Cancer Staging
12:45 - 1:10
Tatheer Hussain Mir
Presenting topic: Leveraging Large Language Models and Multimodal Large Language Models for Cross Modal Deidentification and Temporal Reasoning
1:10 - 1.30
Discussion and Q/A
Topics of Interest
Topics of Interest
Topics of interest include, but are not limited to, the following:
Digital Epidemiology
Biomedical Text Mining
Natural Language Processing in Epidemiology
AI and Machine Learning for Disease Detection
Ethical and Privacy Considerations in Digital Surveillance
Social Media and Big Data Analytics for Public Health
Social Media Perceptions on Usage of AI for Cancer Diagnosis and Treatment
Important Dates
Important Dates
Paper submission deadline: July 31, 2025
Notification of acceptance: August 7, 2025
Revisions due: August 12, 2025
2025 Workshop: August 13, 2025
Publication of accepted papers: December 31, 2025
** All deadlines are calculated at 11:59pm UTC+8
Organizers
Organizers
Yung-Chun Chang
Graduate Institute of Data Science,
Taipei Medical University, Taiwan
Jitendra Jonnagaddala
School of Population Health
University of New South Wales, Australia
📧 jitendra.jonnagaddala@unsw.edu.au
Hong-Jie Dai
Department of Electrical Engineering,
National Kaohsiung University of Science and Technology, Taiwan,
Program Committee Members
Program Committee Members
Hsuan-Chia Yang, Taipei Medical University
Chih-Wei (Grace) Huang, Taipei Medical University
Yu-Kai Wang, University of Technology Sydney
Chih Hao Ku, University of North Texas
Po-Ting Lai, National Institutes of Health
Yoshinobu Kano, Shizuoka University
Venue
Venue
The Taipei International Convention Center (TICC), Taipei, Taiwan
Sponsored by
Sponsored by
(TBA)
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